Control systems for vehicles typically utilize an architecture in which a central processor carries out many instructions at a time. These controllers utilize a so-called neural network which analyzes data by passing the data through several simulated processors which are interconnected with synaptic-like weights. These controllers are generally dedicated to specific functions such as air/fuel control systems, engine control systems, etc. After training with several examples, the network begins to organize itself and refines its own architecture to fit the data, much like a human brain learns from examples. A single neuron of a conventional neural network that typically includes a plurality of neurons is shown in FIG. 1.
However, a conventional neural network system requires extensive training in order for the system to learn from examples and to reorganize. Such a system is not feasible for use in control of complicated machinery subject to many dynamically changing inputs for which the system must first be trained. Accordingly, it would be useful to have an adaptive controller that can perform learning without extensive training.